Performance Comparison of Machine Learning Algorithms for TAB Digit Recognition


KIPS Transactions on Software and Data Engineering, Vol. 8, No. 1, pp. 19-26, Jan. 2019
https://doi.org/10.3745/KTSDE.2019.8.1.19,   PDF Download:
Keywords: Digit Recognition, Machine Learning, Prototype Selection, image processing, Cross-Validation
Abstract

In this paper, the classification performance of learning algorithms is compared for TAB digit recognition. The TAB digits that are segmented from TAB musical notes contain TAB lines and musical symbols. The labeling method and non-linear filter are designed and applied to extract fret digits only. The shift operation of the 4 directions is applied to generate more data. The selected models are Bayesian classifier, support vector machine, prototype based learning, multi-layer perceptron, and convolutional neural network. The result shows that the mean accuracy of the Bayesian classifier is about 85.0% while that of the others reaches more than 99.0%. In addition, the convolutional neural network outperforms the others in terms of generalization and the step of the data preprocessing.


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Cite this article
[IEEE Style]
J. Heo, H. Lee, D. Hwang, "Performance Comparison of Machine Learning Algorithms for TAB Digit Recognition," KIPS Transactions on Software and Data Engineering, vol. 8, no. 1, pp. 19-26, 2019. DOI: https://doi.org/10.3745/KTSDE.2019.8.1.19.

[ACM Style]
Jaehyeok Heo, Hyunjung Lee, and Doosung Hwang. 2019. Performance Comparison of Machine Learning Algorithms for TAB Digit Recognition. KIPS Transactions on Software and Data Engineering, 8, 1, (2019), 19-26. DOI: https://doi.org/10.3745/KTSDE.2019.8.1.19.